Modern multimedia environments generally employ a variety of sensor or data inputs. For example, a gaming environment may include a red-green-blue (RGB) camera to capture an image of a player in a gaming scene and a depth camera to detect the distance between the depth camera and various points in the gaming scene, including points on the player. In this manner, the multimedia environment can determine and interpret characteristics in the captured scene.
In some multimedia applications, the effectiveness of a given sensor is limited to a particular range or condition. For example, in an intense ambient light scenario, an RGB light sensor tends to saturate, and in low ambient light scenarios, an RGB light sensor may not effectively capture sufficient data to locate and process a dark region of interest. To address these performance issues, the resolution or sensitivity of one or more sensors may be adjusted. However, such adjustments can impact other aspects of performance. For example, increasing the resolution of one or more sensors can increase the amount of data captured and increase the computational requirements and/or increase the latency in the multimedia environment.
Additionally, in the context of locating and tracking an object of interest, such as a human user, data captured by a sensor may be ambiguous. The captured sensor data may result in a false positive, which identifies and tracks a non-existent human user, or a false negative, which fails to identify an existing human user. For example, in multimedia environments with RGB or depth sensor based tracking systems, inanimate objects and animals may result in a false positive in conditions outside the optimal RGB and depth sensor ranges. Additionally, a human user may blend into surrounding objects (e.g., a couch), resulting in a false negative. Accordingly, existing multimedia environments may not under certain conditions efficiently focus sensor resources on regions of interest or adequately reduce the ambiguity of objects of interest.